Systems and methods for providing personalized radiation therapy

ABSTRACT

An example method of treating a subject having a tumor is described herein. The method can include determining a radiosensitivity index of the tumor, deriving a subject-specific variable based on the radiosensitivity index, and obtaining a genomic adjusted radiation dose effect value for the tumor. The radiosensitivity index can be assigned from expression levels of signature genes of a cell of the tumor. Additionally, the genomic adjusted radiation dose effect value can be predictive of tumor recurrence in the subject after treatment. The method can also include determining a radiation dose based on the subject-specific variable and the genomic adjusted radiation dose effect value.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. provisional patentapplication No. 62/157,245, filed on May 5, 2015, and entitled “SYSTEMSAND METHODS FOR PROVIDING PERSONALIZED RADIATION THERAPY,” thedisclosure of which is expressly incorporated herein by reference in itsentirety.

STATEMENT REGARDING FEDERALLY FUNDED RESEARCH

This invention was made with government support under Grant no.R21CA101355/R21CA135620 awarded by the National Institutes of Health andGrant no. 170220051 awarded by the US Army Medical Research and MaterialCommand, National Functional Genomics Center. The government has certainrights in the invention.

BACKGROUND

Radiation Therapy (RT) is a highly utilized, efficacious andcost-effective therapeutic option for cancer patients. RT is received byup to two-thirds of all cancer patients in the US, has been estimated tobe responsible for 40% of all cancer cures, yet represents only 5-10% ofall cancer-related health expenditures^(1,2). In spite of itstherapeutic importance, it is under-represented in the nationalportfolio of clinical trials (i.e. only 5.5% of NCI trials involve RT)².

The sequencing of the human genome has paved the way for the era ofprecision medicine which promises that the right treatment will bedelivered to the right patient at the right time. While the genomic erahas affected the delivery of chemotherapy and targeted biologicalagents³ ⁴ ⁵, it has yet to impact RT, the single most utilizedtherapeutic agent in oncology⁶.

A central principle in precision medicine is that cancer therapy shouldbe tailored to individual tumor biology⁷ ⁸ ⁹. In spite of this tenet, RTdose protocols are uniform or one-size-fits-all (e.g., a uniform dailydose rate of 2 Gray (“Gy”)) and have not yet been adapted to thisvision. Thus, integrating individual biological differences into RTprotocols is a central step towards realizing the promise of precisionmedicine, thereby improving RT-based clinical outcomes. Previously, agene-expression based radiosensitivity index (RSI) was developed thathas been validated in over 2,000 patients as a predictor of clinicaloutcome in RT-treated patients in multiple independent cohorts anddisease sites¹⁰⁻¹⁹. These data support that clinical benefit from RT isnon-uniform and only maximized in a sub-population ofgenomically-distinct patients (e.g. radiosensitive).

Personalized RT holds the promise that the diagnosis, prevention, andtreatment of cancer can be based on individual assessment of risk.

SUMMARY

Systems and methods for providing personalized radiation therapy aredescribed herein. For example, a radiosensitivity index (“RSI”), whichis a molecular signature derived from cellular survival, can be used tocustomize radiation therapy for an individual subject. RSI canoptionally be used to prescribe (and optionally administer) apersonalized radiation dose to the subject. For example, using the RSI,a particular radiation dose per treatment and/or a particular number ofradiation therapy treatments (or fractionation) can be prescribed for(and optionally administered to) the subject in order to reduce thelikelihood of tumor reoccurrence after radiation treatment.

An example method of treating a subject having a tumor is describedherein. The method can include determining a radiosensitivity index ofthe tumor, deriving a subject-specific variable based on theradiosensitivity index, and obtaining a genomic adjusted radiation doseeffect value for the tumor. The radiosensitivity index can be assignedfrom expression levels of signature genes of a cell in the tumor. Thesignature genes can include, but are not limited to, Androgen receptor(AR); Jun oncogene (c-Jun); Signal transducer and activator oftranscription 1 (STAT1); Protein kinase C, beta (PRKCB or PKC); V-relreticuloendotheliosis viral oncogene homolog A (avian) (RFA or p65);c-Abl oncogene 1, receptor tyrosine kinase (ABL1 or c-Abl); SMT3suppressor of mif two 3 homolog 1 (S. cerevisiae) (SUMO1); p21(CDKN1A)-activated kinase 2 (PAK2); Histone deacetylase 1 (HDAC1);and/or Interferon regulatory factor 1 (IRF1). Additionally, the genomicadjusted radiation dose effect value can be predictive of tumorrecurrence in the subject after treatment. The method can also includedetermining a radiation dose based on the subject-specific variable andthe genomic adjusted radiation dose effect value. The radiation dose canbe defined by a radiation dose per treatment and a number of radiationtreatments (or fractionation). Optionally, the method can furtherinclude administering radiation therapy to the subject at the radiationdose.

An example system for developing a radiation therapy treatment plan fora subject having a tumor is also described herein. The system caninclude a processor and a memory operably coupled to the processor. Thememory can have computer-executable instructions stored thereon that,when executed by the processor, cause the processor to determine aradiosensitivity index of the tumor, derive a subject-specific variablebased on the radiosensitivity index, and obtain a genomic adjustedradiation dose effect value for the tumor. The radiosensitivity indexcan be assigned from expression levels of signature genes of a cell inthe tumor. The signature genes can include, but are not limited to,Androgen receptor (AR); Jun oncogene (c-Jun); Signal transducer andactivator of transcription 1 (STAT1); Protein kinase C, beta (PRKCB orPKC); V-rel reticuloendotheliosis viral oncogene homolog A (avian) (RELAor p65); c-Abl oncogene 1, receptor tyrosine kinase (ABL1 or c-Abl);SMT3 suppressor of mif two 3 homolog 1 (S. cerevisiae) (SUMO1); p21(CDKN1A)-activated kinase 2 (PAK2); Histone deacetylase 1 (HDAC1);and/or Interferon regulatory factor 1 (IRF1). Additionally, the genomicadjusted radiation dose effect value can be predictive of tumorrecurrence in the subject after treatment. The memory can have furthercomputer-executable instructions stored thereon that, when executed bythe processor, cause the processor to determine a radiation dose basedon the subject-specific variable and the genomic adjusted radiation doseeffect value. The radiation dose can be defined by a radiation dose pertreatment and a number of radiation treatments (or fractionation).

As described above, the radiation dose can be defined by the number ofradiation treatments and the radiation dose per radiation treatment,e.g., the number of radiation treatments times the radiation dose pertreatment. Optionally, determining a radiation dose can includedetermining the number of radiation treatments. Optionally, determininga radiation dose can include determining the radiation dose pertreatment. Optionally, the radiation dose per treatment can be thestandard clinical dose. For example, the radiation dose per treatmentcan be approximately 2 Gray (“Gy”). It should be understood that theradiation dose per treatment can be another dosage, e.g., more or lessthan 2 Gy.

Alternatively or additionally, the genomic adjusted radiation doseeffect value for the tumor can optionally be a range of valuespredictive of tumor recurrence in the subject after treatment.

Alternatively or additionally, the genomic adjusted radiation doseeffect value for the tumor can optionally be indicative of a low chanceof tumor recurrence in the subject after treatment.

Alternatively or additionally, the genomic adjusted radiation doseeffect value for the tumor can optionally be specific to a type ofcancer. For example, the type of cancer can include, but is not limitedto, breast, lung, prostate, glioblastoma, head and neck, pancreas,esophagus, or colorectal cancer. It should be understood that the typeof cancer can be a type of cancer other than those listed herein.

Alternatively or additionally, the genomic adjusted radiation doseeffect value can optionally be determined by analyzing the respectivetreatment plans and outcomes for a group of subjects (e.g., a pluralityof subjects). The analysis can optionally be performed retrospectively.For example, a univariate or multivariate analysis of genomic doseeffect values and outcomes for a group of subjects that have receivedradiation treatment can optionally be performed.

Alternatively or additionally, the subject-specific variable canoptionally provide a measure of the tumor's ability to accumulateradiation damage.

Alternatively or additionally, the subject-specific variable canoptionally be derived using a linear quadratic model for cell survival.The radiosensitivity index can be approximately equal to cell survival(e.g., cell survival at a radiation dose of 2 Gy).

It should be understood that the above-described subject matter may beimplemented as a computer-controlled apparatus, a computer process, acomputing system, or an article of manufacture, such as acomputer-readable storage medium.

Other systems, methods, features and/or advantages will be or may becomeapparent to one with skill in the art upon examination of the followingdrawings and detailed description. It is intended that all suchadditional systems, methods, features and/or advantages be includedwithin this description and be protected by the accompanying claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The components in the drawings are not necessarily to scale relative toeach other. Like reference numerals designate corresponding partsthroughout the several views.

FIG. 1 is a flow diagram illustrating example operations for treating asubject having a tumor.

FIG. 2 is an example computing device.

FIG. 3 is a flow diagram illustrating example operations for determininga personalized radiation dose using RSI.

FIG. 4 is a flow diagram illustrating example operations for developinga personalized radiation treatment plan for a subject.

FIG. 5 is a table showing the results of the evaluation of six clinicalcohorts of subjects.

FIG. 6 is a diagram illustrating derivation of RSI from a tumor sampleor biopsy (top panel) and derivation of GARD from RSI (middle panel). Inthe bottom panel, the distribution of RSI, α, and GARD are shown for acohort of 263 patients in the Erasmus Breast Cancer cohort.

FIGS. 7A-7G illustrate a framework for genomic RT dose with reference tothe TCC protocol described herein.

FIGS. 8A-8G illustrate a framework for genomic RT dose with reference tothe Erasmus Breast Cancer cohort described herein.

FIG. 9 is a table illustrating the multivariable analysis of GARD in theErasmus Breast Cancer cohort.

FIGS. 10A-10C are graphs illustrating genomically-informed RT.

DETAILED DESCRIPTION

Unless defined otherwise, all technical and scientific terms used hereinhave the same meaning as commonly understood by one of ordinary skill inthe art. Methods and materials similar or equivalent to those describedherein can be used in the practice or testing of the present disclosure.As used in the specification, and in the appended claims, the singularforms “a,” “an,” “the” include plural referents unless the contextclearly dictates otherwise. The term “comprising” and variations thereofas used herein is used synonymously with the term “including” andvariations thereof and are open, non-limiting terms. The terms“optional” or “optionally” used herein mean that the subsequentlydescribed feature, event or circumstance may or may not occur, and thatthe description includes instances where said feature, event orcircumstance occurs and instances where it does not. Whileimplementations will be described for treating a subject having a tumor,it will become evident to those skilled in the art that theimplementations are not limited thereto.

The methods described herein can be used to treat, or develop atreatment plan for, any solid tumor in a subject. A solid tumor is anabnormal mass of hyperproliferative or neoplastic cells from a tissueother than blood, bone marrow, or the lymphatic system, which may bebenign or cancerous. In general, the tumors treated by the methodsdescribed herein are cancerous. As used herein, the terms“hyperproliferative” and “neoplastic” refer to cells having the capacityfor autonomous growth, i.e., an abnormal state or conditioncharacterized by rapidly proliferating cell growth. Hyperproliferativeand neoplastic disease states may be categorized as pathologic, i.e.,characterizing or constituting a disease state, or may be categorized asnon-pathologic, i.e., a deviation from normal but not associated with adisease state. The term is meant to include all types of solid cancerousgrowths, metastatic tissues or malignantly transformed cells, tissues,or organs, irrespective of histopathologic type or stage ofinvasiveness. “Pathologic hyperproliferative” cells occur in diseasestates characterized by malignant tumor growth. Examples ofnon-pathologic hyperproliferative cells include proliferation of cellsassociated with wound repair. Examples of solid tumors are sarcomas,carcinomas, and lymphomas. Leukemias (cancers of the blood) generally donot form solid tumors.

The term “carcinoma” is art recognized and refers to malignancies ofepithelial or endocrine tissues including respiratory system carcinomas,gastrointestinal system carcinomas, genitourinary system carcinomas,testicular carcinomas, breast carcinomas, prostatic carcinomas,endocrine system carcinomas, and melanomas. In some implementations, thedisease is lung carcinoma, rectal carcinoma, colon carcinoma, esophagealcarcinoma, prostate carcinoma, head and neck carcinoma, or melanoma.Exemplary carcinomas include those forming from tissue of the cervix,lung, prostate, breast, head and neck, colon and ovary. The term alsoincludes carcinosarcomas, e.g., which include malignant tumors composedof carcinomatous and sarcomatous tissues. An “adenocarcinoma” refers toa carcinoma derived from glandular tissue or in which the tumor cellsform recognizable glandular structures.

The term “sarcoma” is art recognized and refers to malignant tumors ofmesenchymal derivation.

In some implementations, the tumors treated by a method described hereinare of epithelial cell origin. In some implementations, the tumorsoriginate from lung, colon, rectal, esophageal, prostate, or head/necktissues (e.g., originating from the upper aerodigestive tract, includingthe lip, oral cavity, nasal cavity, paranasal sinuses, pharynx, andlarynx, e.g., squamous cell carcinomas originating from the mucosallining (epithelium)). In some implementations, the tumors aremetastatic, and originate from an epithelial tissue (and are thusepithelial in origin) but have spread to another tissue, e.g.,epithelial-origin prostate cancer that has spread to the bones of thepelvis, spine and/or ribs, or lung carcinoma that has metastasized tothe adrenal glands, liver, brain, or bones.

Referring now to FIG. 1, example operations 100 for treating a subjecthaving a tumor are described. At 102, a radiosensitivity index (“RSI”)of the tumor is determined. RSI can be assigned from expression levelsof one or more signature genes of a cell or cells in the subject'stumor. This disclosure contemplates that RSI can be determined using acomputing device, for example. One or more assays of cell(s) of thesubject's tumor can be performed to determine gene expression levels.For example, any known technique for obtaining a sample comprising atleast one living cell (preferably a plurality of cells), e.g., a cellfrom the subject's tumor (e.g., from a biopsy) can be used. Commonlyused methods to obtain tumor cells include surgical (e.g., the use oftissue taken from the tumor after removal of all or part of the tumor)and needle biopsies. The samples should be treated in any way thatpreserves intact the gene expression levels of the living cells as muchas possible, e.g., flash freezing or chemical fixation, e.g., formalinfixation. Additionally, any known technique can be used to extractmaterial, e.g., protein or nucleic acid (e.g., mRNA) from the sample.For example, mechanical or enzymatic cell disruption can be used,followed by a solid phase method (e.g., using a column) orphenol-chloroform extraction, e.g., guanidiniumthiocyanate-phenol-chloroform extraction of the RNA. A number of kitsare commercially available for use in isolation of mRNA.

The signature genes can include, but are not limited to, Androgenreceptor (AR); Jun oncogene (c-Jun); Signal transducer and activator oftranscription 1 (STAT1); Protein kinase C, beta (PRKCB or PKC); V-relreticuloendotheliosis viral oncogene homolog A (avian) (RELA or p65);c-Abl oncogene 1, receptor tyrosine kinase (ABL1 or c-Abl); SMT3suppressor of mif two 3 homolog 1 (S. cerevisiae) (SUMO1); p21(CDKN1A)-activated kinase 2 (PAK2); Histone deacetylase 1 (HDAC1);and/or Interferon regulatory factor 1 (IRF1). It should be understoodthat the signature genes can include one or more other genes not listedabove, which are provided only as examples. For example, RSI can beassigned using a linear regression model of gene expression levels asdescribed in U.S. Pat. No. 8,660,801 to Torres-Roca et al., issued Feb.25, 2014, entitled “Gene signature for the prediction of radiationtherapy response,” the disclosure of which is incorporated by referencein its entirety herein. As described therein, RSI provides an indicationof whether radiation therapy is likely to be effective in treating thesubject's tumor. RSI has a value approximately between 0 and 1. Eschrichet al., Systems biology modeling of the radiosensitivity network: abiomarker discovery platform, Int. J. Radiat. Oncol. Biol. Phys. (2009).It should be understood that assigning RSI according to the linearregression model of gene expression levels described in U.S. Pat. No.8,660,801 is provided only as an example and that other known techniquesfor assigning radiation sensitivity can optionally be used with thesystems and methods described herein.

Example methods for determining RSI use a rank-based linear algorithm toassign an RSI to a cell, e.g., a living cell such as a tumor cell from apatient, a normal cell from a patient, or a cultured cell. In general,the methods are applicable to any mammal, particularly humans. Themethods include determining expression levels of signature genes in acell or cells of the tumor, and determining a RSI based on theexpression levels. In some implementations, the methods include the useof two or more, e.g., three, four, five, six, seven, eight, nine, or allten signature genes as shown in Table 1.

TABLE 1 Gene Name Androgen receptor c-Jun STAT1 PKC RelA (p65) c-AblSUMO-1 PAK2 HDAC1 IRF1

Although the exemplary gene sequences set forth above are for the humangenes, and thus are best suited for use in human cells, one of skill inthe art could readily identify mammalian homologs using databasesearches (for known sequences) or routine molecular biologicaltechniques (to identify additional sequences). In general, genes areconsidered homologs if they show at least 80%, e.g., 90%, 95%, or more,identity in conserved regions (e.g., biologically important regions).

A linear regression model useful in the methods described hereinincludes gene expression levels and coefficients, or weights, forcombining expression levels. The coefficients can be calculated using aleast-squares fit of the proposed model to a measure of cellularradiation sensitivity. One example described herein used the survivalfraction at 2 Gy (“SF2”) although other measures at other dose levels(e.g., SF8) can be considered with different coefficients beingdetermined from each. The functional form of the algorithm is givenbelow, wherein each of the k_(i) coefficients will be determined byfitting expression levels to a particular RSI measure.

RSI=k ₁*AR+k ₂*c-jun+k ₃*STAT1+k ₄*PKC+k ₅*RelA+k ₆*cAB1+k ₇*SUMO1+k₈*PAK2+k ₉*HDAC+k ₁₀*IRF1

In some implementations, the methods include applying an algorithm toexpression level data determined in a cell; e.g., a rank-based linearregression algorithm as described herein. In some implementations, thealgorithm includes weighting coefficients for each of the genes.

At 104, a subject-specific variable can be derived based on RSI. Thisdisclosure contemplates that the subject-specific variable can bederived using a computing device, for example. The subject-specificvariable can optionally be derived using a linear quadratic model forcell survival. For example, RSI is a molecular estimate of the survivalfraction at 2 Gy (“SF2”). RSI can therefore be substituted for Survivalin the standard linear quadratic model for cell survival as shown inEqn. (1) below.

RSI=e ^(−ad−βd̂2),  (1)

where α and β are variables that provide measures of a tumor's abilityto accumulate radiation damage, and d is the radiation dose (e.g., theradiation dose per treatment as used herein).

Using Eqn. (1), and assuming β is a constant for standard fractionationand d is 2 Gy (e.g., the standard clinical dose), the subject-specificvariable (e.g., a) can be derived after determining RSI (e.g., the RSIdetermined at 102). For example, β is assumed to be constant and can beobtained using techniques known in the art, for example, as described inLea D E. Actions of Radiation on Living Cells. Cambridge: UniversityPress; 1946. It should be understood that RSI can be determined at otherdose level. (e.g., SF8). In these cases, d would have a value more orless than 2 Gy in Eqn. (1). In other words, although the value of d isdependent on the RSI determination, the value of d is known. The derivedsubject-specific variable (e.g., a) can then be used to determine thedesired radiation dose as described below.

At 106, a genomic adjusted radiation dose effect value for the tumor isobtained. This disclosure contemplates that the genomic adjustedradiation dose effect value can be obtained using a computing device,for example. The genomic adjusted radiation dose effect value can bepredictive of tumor recurrence in the subject after treatment. Thegenomic adjusted radiation dose effect value for the tumor canoptionally be indicative of a low chance of tumor recurrence in thesubject after treatment. Optionally, the genomic adjusted radiation doseeffect value for the tumor can optionally be a range of values. As usedherein, genomic adjusted radiation dose effect (“GARD”) is a measure ofeffectiveness of radiation therapy. A higher GARD implies a higherpredicted radiation therapy effect. A lower GARD implies a lowerpredicted radiation therapy effect. GARD is specific to a type ofcancer, e.g., including, but not limited to, breast, lung, prostate,glioblastoma, head and neck, pancreas, esophagus, or colorectal cancer.In other words, GARD high/GARD low values (or range of values) arespecific to a type of cancer, as well as the specific clinicalindication. In some implementations, the GARD value for a particulartype of cancer has been predetermined and is stored in memory of acomputing device for later reference. In other implementations, the GARDvalue for a particular type of cancer is determined and then optionallystored in the memory of a computing device for later reference.

It should be understood that GARD high/GARD low values (or range ofvalues) can be determined (e.g., calculated) by analyzing GARD andoutcome for a group of subjects (e.g., a plurality of subjects) havingthe same type of cancer. GARD can optionally be determined by analyzingthe respective treatment plans (e.g., dose per treatment, number oftreatments/fractionation, etc.) for the group of subjects with knownoutcomes (e.g., distant metastasis-free survival (“DMFS”), overallsurvival (“OS”), etc.). The analysis can optionally be performedretrospectively. For example, a univariate or multivariate analysis canoptionally be performed to obtain GARD high/GARD low values for thegroup of subjects. The analysis can reveal a particular GARD value (orrange of values) that is predicted to achieve a positive outcome. Inother words, the analysis can be used to determine a particular GARDvalue (or range of values) that reduces a subject's risk of tumorreoccurrence after radiation treatment. It should be understood that theparticular GARD value (or range of values) can be used prospectively inthe treatment of a subject.

Then, at 108, a radiation dose can be determined based on thesubject-specific variable (e.g., a) and the genomic adjusted radiationdose effect value. This disclosure contemplates that the radiation dosecan be determined using a computing device, for example. The radiationdose can be determined by the radiation dose per treatment (e.g., 2 Gy)and the number of radiation treatments. For example, the radiation dosecan be determined by the number of radiation treatments times the doseper radiation treatment. As described below, when the radiation dose pertreatment is known (e.g., a standard dose of 2 Gy), the number ofradiation treatments (or fractionation) can be determined or selected toachieve a particular GARD value for the subject, for example a high GARDvalue that likely reduces the subject's risk of tumor reoccurrence afterradiation therapy. GARD is a subject-specific measure of theradiobiology parameter for dose effect shown in Eqn. (2) below.

GARD=nd(α+βd),  (2)

where α and β are variables that provide measures of a tumor's abilityto accumulate radiation damage, d is the radiation dose (e.g., theradiation dose per treatment as used herein), and n is the number ofradiation treatments (or fractionation).

Eqn. (2) can be used to determine the number of radiation treatments.Specifically, the GARD value obtained at 106 is predictive of tumorrecurrence in the subject after treatment, and optionally indicative ofa low chance of tumor recurrence in the subject after treatment.Additionally, β is a constant for standard fractionation, d is 2 Gy(e.g., the standard clinical dose), and a (e.g., the subject-specificvariable) is derived at 104. In other words, using Eqn. (2), the numberof radiation treatments (or fractionation) for achieving a predictedoutcome can be determined. In this way, the radiation treatment ispersonalized for the subject. Optionally, radiation therapy isadministered to the subject at the radiation dose per treatment (e.g., 2Gy) and/or the number of radiation treatments (e.g., n determined at108).

It should be appreciated that the logical operations described hereinwith respect to the various figures may be implemented (1) as a sequenceof computer implemented acts or program modules (i.e., software) runningon a computing device, (2) as interconnected machine logic circuits orcircuit modules (i.e., hardware) within the computing device and/or (3)a combination of software and hardware of the computing device. Thus,the logical operations discussed herein are not limited to any specificcombination of hardware and software. The implementation is a matter ofchoice dependent on the performance and other requirements of thecomputing device. Accordingly, the logical operations described hereinare referred to variously as operations, structural devices, acts, ormodules. These operations, structural devices, acts and modules may beimplemented in software, in firmware, in special purpose digital logic,and any combination thereof. It should also be appreciated that more orfewer operations may be performed than shown in the figures anddescribed herein. These operations may also be performed in a differentorder than those described herein.

When the logical operations described herein are implemented insoftware, the process may execute on any type of computing architectureor platform. For example, referring to FIG. 2, an example computingdevice upon which embodiments of the invention may be implemented isillustrated. The computing device 200 may include a bus or othercommunication mechanism for communicating information among variouscomponents of the computing device 200. In its most basic configuration,computing device 200 typically includes at least one processing unit 206and system memory 204. Depending on the exact configuration and type ofcomputing device, system memory 204 may be volatile (such as randomaccess memory (RAM)), non-volatile (such as read-only memory (ROM),flash memory, etc.), or some combination of the two. This most basicconfiguration is illustrated in FIG. 2 by dashed line 202. Theprocessing unit 206 may be a standard programmable processor thatperforms arithmetic and logic operations necessary for operation of thecomputing device 200.

Computing device 200 may have additional features/functionality. Forexample, computing device 200 may include additional storage such asremovable storage 208 and non-removable storage 210 including, but notlimited to, magnetic or optical disks or tapes. Computing device 200 mayalso contain network connection(s) 216 that allow the device tocommunicate with other devices. Computing device 200 may also have inputdevice(s) 214 such as a keyboard, mouse, touch screen, etc. Outputdevice(s) 212 such as a display, speakers, printer, etc. may also beincluded. The additional devices may be connected to the bus in order tofacilitate communication of data among the components of the computingdevice 200. All these devices are well known in the art and need not bediscussed at length here.

The processing unit 206 may be configured to execute program codeencoded in tangible, computer-readable media. Computer-readable mediarefers to any media that is capable of providing data that causes thecomputing device 200 (i.e., a machine) to operate in a particularfashion. Various computer-readable media may be utilized to provideinstructions to the processing unit 206 for execution. Common forms ofcomputer-readable media include, for example, magnetic media, opticalmedia, physical media, memory chips or cartridges, a carrier wave, orany other medium from which a computer can read. Examplecomputer-readable media may include, but is not limited to, volatilemedia, non-volatile media and transmission media. Volatile andnon-volatile media may be implemented in any method or technology forstorage of information such as computer readable instructions, datastructures, program modules or other data and common forms are discussedin detail below. Transmission media may include coaxial cables, copperwires and/or fiber optic cables, as well as acoustic or light waves,such as those generated during radio-wave and infra-red datacommunication. Example tangible, computer-readable recording mediainclude, but are not limited to, an integrated circuit (e.g.,field-programmable gate array or application-specific IC), a hard disk,an optical disk, a magneto-optical disk, a floppy disk, a magnetic tape,a holographic storage medium, a solid-state device, RAM, ROM,electrically erasable program read-only memory (EEPROM), flash memory orother memory technology, CD-ROM, digital versatile disks (DVD) or otheroptical storage, magnetic cassettes, magnetic tape, magnetic diskstorage or other magnetic storage devices.

In an example implementation, the processing unit 206 may executeprogram code stored in the system memory 204. For example, the bus maycarry data to the system memory 204, from which the processing unit 206receives and executes instructions. The data received by the systemmemory 204 may optionally be stored on the removable storage 208 or thenon-removable storage 210 before or after execution by the processingunit 206.

Computing device 200 typically includes a variety of computer-readablemedia. Computer-readable media can be any available media that can beaccessed by device 200 and includes both volatile and non-volatilemedia, removable and non-removable media. Computer storage media includevolatile and non-volatile, and removable and non-removable mediaimplemented in any method or technology for storage of information suchas computer readable instructions, data structures, program modules orother data. System memory 204, removable storage 208, and non-removablestorage 210 are all examples of computer storage media. Computer storagemedia include, but are not limited to, RAM, ROM, electrically erasableprogram read-only memory (EEPROM), flash memory or other memorytechnology, CD-ROM, digital versatile disks (DVD) or other opticalstorage, magnetic cassettes, magnetic tape, magnetic disk storage orother magnetic storage devices, or any other medium which can be used tostore the desired information and which can be accessed by computingdevice 200. Any such computer storage media may be part of computingdevice 200.

It should be understood that the various techniques described herein maybe implemented in connection with hardware or software or, whereappropriate, with a combination thereof. Thus, the methods andapparatuses of the presently disclosed subject matter, or certainaspects or portions thereof, may take the form of program code (i.e.,instructions) embodied in tangible media, such as floppy diskettes,CD-ROMs, hard drives, or any other machine-readable storage mediumwherein, when the program code is loaded into and executed by a machine,such as a computing device, the machine becomes an apparatus forpracticing the presently disclosed subject matter. In the case ofprogram code execution on programmable computers, the computing devicegenerally includes a processor, a storage medium readable by theprocessor (including volatile and non-volatile memory and/or storageelements), at least one input device, and at least one output device.One or more programs may implement or utilize the processes described inconnection with the presently disclosed subject matter, e.g., throughthe use of an application programming interface (API), reusablecontrols, or the like. Such programs may be implemented in a high levelprocedural or object-oriented programming language to communicate with acomputer system. However, the program(s) can be implemented in assemblyor machine language, if desired. In any case, the language may be acompiled or interpreted language and it may be combined with hardwareimplementations.

Referring now to FIG. 3, a flow diagram illustrating example operationsfor determining a personalized radiation dose using RSI is shown. InFIG. 3, at 302, a subject-specific variable (e.g., a) is derived basedon RSI, which is a molecular estimate of the survival fraction at 2 Gy.Using the subject-specific variable, at 304, the number of radiationtreatments (e.g., n) to achieve a predetermined GARD, assuming the othervalues (e.g., dose per radiation treatment (d) and β) are known.

Referring now to FIG. 4, a flow diagram illustrating example operationsfor developing a personalized radiation treatment plan for a subject isshown. During the radiation treatment phase 400 of FIG. 4, at 402, athreshold GARD can optionally be determined by analyzing the respectivetreatment plans (e.g., dose per treatment, number oftreatments/fractionation, etc.) for a group of subjects with knownoutcomes (e.g., distant metastasis-free survival (“DMFS”), overallsurvival (“OS”), etc.). At 404, a subject-specific variable (e.g., α) isderived based on RSI, which is a molecular estimate of the survivalfraction at 2 Gy. Using the subject-specific variable, at 406, thenumber of radiation treatments (e.g., n) to achieve the threshold GARD,assuming the other values (e.g., dose per radiation treatment (d) and β)are known.

Examples

According to implementations described herein, RSI together withestablished radiobiological principles serve as the basis for precisionmedicine in radiation oncology. A genomic-adjusted radiation dose (GARD)can be derived by integrating a patient-specific RSI with physical RTdose and fractionation using the linear quadratic model. As described indetail below, it has been demonstrated, in a cohort of 8,271 patientsacross 20 different disease sites, that GARD exhibits wide heterogeneityboth within and across solid tumor types in spite of uniform RI dose.Further, it has been shown that GARD is a superior predictor of clinicaloutcome compared to all variables including RSI in a cohort of breastcancer patients. Finally, it has been shown that GARD model identifiessub-populations that derive differential benefit from RI and can beutilized to individualize RT dose to optimize outcome.

GARD was evaluated in six independent clinical cohorts of subjects(e.g., patients) who received radiation therapy (“RI”) (standardfractionation, FIG. 5). As shown in FIG. 5, the cohorts included threedifferent breast cancer cohorts (e.g., n=77, 263, and 75, respectively,where n is the number of subjects), a lung cancer cohort (n=60), aglioblastoma cancer cohort (n=98), and a pancreatic cancer cohort(n=40). Gene expression was available from public sources or from theinstitutional bank of H. Lee Moffitt Cancer Center and ResearchInstitute. RSI was calculated as described herein. Primary endpointsevaluated include recurrence free survival (“RFS”), distantmetastasis-free survival (“DMFS”), local control (“LC”) and overallsurvival (“OS”). GARD was compared to DMFS, LC or OS using univariate(“UVA”) and multivariable (“MVA”) Cox proportional hazard models.

A broad RSI distribution was observed for all cohorts, which leads to alarge range of GARD values with clinically-relevant radiation doses. OnUVA, GARD-low patients have worse outcome in five of the six cohortsthat is statistically significant. The exception being the pancreaticcancer cohort. On MVA, GARD predicts outcome in all six cohorts. Onepossible reason why UVA and MVA yield different results as to whetherGARD predicts outcome in the pancreatic cancer cohort is that a variablemay not predict by itself because there is some other characteristic inthe cohort of patients that opposes the effect of the variable. However,when the effect of all variables are taken into account, then thepredictive value of the variable is revealed. Accordingly, MVA mayprovide the more important prediction. Additionally, it is estimatedthat a significant proportion of GARD-low patients in each cohort(8%-35%) would have met the threshold for the GARD-high group withcustomized and safe dose escalation.

Materials and Methods

Total Cancer Care (TCC) is a prospective IRB-approved tissue collectionprotocol active at H. Lee Moffitt Cancer Center and Research Instituteand 17 other institutions since 2006²⁰. Tumors from patients enrolled inTCC protocol were arrayed on Affymetrix Hu-RSTA-2a520709 (Affymetrix,Santa Clara, Calif.), which contains approximately 60,000 probesetsrepresenting 25,000 genes. Chips were normalized using iterativerank-order normalization (IRON)²¹. Dimensionality was reduced usingpartial-least squares (PLS). For this analysis, the normalized andde-batched expression values for 13,638 samples from 60 sites of originand the ten RSI-genes were extracted from the TCC database. Allmetastatic, duplicate samples and disease sites with less than 25samples were excluded. This resulted in 8,271 total samples from 20sites of origin.

Erasmus Breast Cancer Cohort: The study was approved by the MedicalEthics Committee of the Erasmus Medical Center. Primary treatment wasbreast conserving therapy in 282 patients (lumpectomy+RT) and mastectomyalone for 62 patients. Detailed radiation records were available for 263patients and these became the study population. Patients received wholebreast RT with or without a boost to the tumor cavity, with total dosesranging from 45-74 Gy delivered 1.8-2 Gy per fraction. The distributionof clinical variables between the excluded patients and the final cohortwere compared. Early metastasis was defined as a distant recurrence inthe first 5 years following completion of primary treatment. Raw geneexpression data was available in GEO (GSE2034, GSE5327).

Radiosensitivity Index (RSI)—RSI scores for the Erasmus dataset werepreviously generated²². Linear scaling was performed to avoid negativeRSI values. Briefly, RSI was previously trained in 48 cancer cell linesto predict cellular radiosensitivity as determined by survival fractionat 2 Gy (SF2)¹². Each of ten genes in the algorithm is ranked based ongene expression (highest expressed gene is ranked at 10 and lowest at1), and RSI was calculated using the pre-determined algorithm below:

RSI=−0.0098009*AR+0.0128283*cJun+0.0254552*STAT1−0.0017589*PKC−0.0038171*RelA+0.1070213*cABL−0.0002509*SUMO1−0.0092431*PAK2−0.0204469*HDAC1−0.0441683*IRF1

This disclosure contemplates using other techniques for assigningradiation sensitivity with the systems and methods described herein, andtherefore, this disclosure should not be limited to calculating RSIaccording to the algorithm provided above, which was used in the examplestudy.

Biologically Effective Dose (BED)—BED was calculated assuming a constantα/β ratio of 2.88 for breast cancer as previously described^(23,24).

Genomic Adjusted Radiation Dose (GARD) GARD is derived using the linearquadratic (LQ) model, the individual RSI and the radiation dose andfractionation schedule for each patient.

Referring now to FIG. 6, a diagram illustrating derivation of RSI from atumor sample or biopsy (top panel) and derivation of GARD from RSI(middle panel) is shown. Gene expression is determined for 10 specificgenes, and a rank-based linear algorithm (e.g., the linear quadraticmodel shown by Eqn. 1 in FIG. 6) is utilized to calculate RSI. Forexample, RSI is substituted for S in the linear quadratic model of Eqn.1 of FIG. 6, and a patient specific α is calculated assuming a β(0.05/Gy²), n=1 and d=2 Gy as shown by Eqn. 2 of FIG. 6. GARD is thencalculated based on Eqn. 3 of FIG. 6, using the patient-specific α andthe RT dose and fractionation received by each individual. The curve inthe middle panel shows the non-linear relationship between RSI and GARDcalculated for a single 2 Gy dose of RT. In the bottom panel, thedistribution of RSI, α and GARD are shown for a cohort of 263 patientsin the Erasmus Breast Cancer Cohort.

As shown by Eqn. 1 of FIG. 6, the LQ model in its simplest form isrepresented by:

S=e ^(−nd(α+βd)),  (3)

where n is the number of fractions of radiation, d is the dose perfraction and α and β represent the linear and quadratic radiosensitivityparameters, respectively.

Since RSI is a molecular estimate of SF2 in cell lines¹², apatient-specific a is derived by substituting RSI for Survival (S) inequation (3), where dose (d) is 2 Gy, n=1 and β is a constant(0.05/Gy²)²⁵. GARD is calculated using the classic equation for biologiceffect shown by equation (2) above (i.e., E=nd(α+βd)), thepatient-specific a and the radiation dose and fractionation received byeach patient.

Statistical analyses—For the TCC analysis, differences in median GARDbetween disease sites were assessed using the Fisher Exact test. For theErasmus dataset analysis, Distant Metastasis-Free Survival (DMFS) wasestimated using the Kaplan-Meier method and the log-rank test was usedto identify differences by GARD, dichotomized at the 75^(th) percentile.This cut-point was pre-determined based on prior RSI analyses′. Theassociation between DMFS with GARD grouping was assessed withmultivariable Cox proportional hazards regression, adjusting forpotential confounders and using a backward elimination model with asignificant level-to-stay of 0.10. When comparing socio-demographic andclinico-pathological characteristics between the final Erasmus cohortand excluded patients, Fisher's Exact test was used to comparecategorical variables including RSI, and Wilcoxon rank sum test forcontinuous variables. All analyses were conducted with SAS (version 9.3)of SAS Institute Inc. of Cary, N.C. and tests were two-sided with asignificance level of 0.05.

The predicted benefit of RT dose escalation by the GARD-based model wascalculated to be:

$\frac{{a*{HR}} + {\left( {1 - a} \right)*1}}{{b*{HR}} + {\left( {1 - b} \right)*1}},$

where a and b are the estimated percentage of patients that achieve thehighest GARD dose level at physical RT dose range of 45-75 Gy. The HR(DM) for GARD-high patients was derived from the multivariable analysisof the Erasmus cohort (HR=2.11 or 0.47).

Results

FIGS. 7A-7G illustrate a framework for genomic RT dose with reference tothe TCC protocol described herein. GARD was calculated for 8,271patients across 20 disease sites in TCC. FIG. 7A illustratestransforming physical radiation dose to genomic adjusted radiation dose(GARD). Standard RT doses for sub-clinical (45 Gy, black), microscopic(60 Gy, white) and macroscopic (>70 Gy, gray) disease are represented asdiscrete uniform blocks with the size of each block proportional to thenumber of patients in each group in TCC (30.4% for 45 Gy (black), 59%for 60 Gy (white) and 10.6% for >70 Gy (gray)). GARD values for eachindividual patient in the TCC cohort are presented ranked from thehighest to lowest value. Each line in the GARD prism represents anindividual patient and is colored based on the physical dose used tocalculate GARD. These data demonstrate that significant heterogeneity inGARD results from uniform, one-size-fits all RT dose. In FIGS. 7B-7D,three GARD levels (low 0-30.4th percentile, middle 30.41th-89.4thpercentile, and high 89.41-100 percentile) are defined to correspond tothe same proportion of patients represented for each RT dose. Pie chartsare shown demonstrating the proportion of patients at each physical doselevel (45 Gy, black, 60 Gy, white and (>70 Gy, gray) in each GARD level.All physical doses are represented in each of the GARD levels. FIG. 7Brepresents the distribution of physical doses in the highest GARD level(top 10.6% of GARD scores), FIG. 7C represents the distribution ofphysical doses in the middle GARD level (30.41st-89.4th percentile ofGARD scores), and FIG. 7D represents the distribution of physical dosesin the lowest GARD level (bottom 30.4th percentile). FIG. 7E-7G presentthe GARD distribution for each disease site within each RT dose levelted. FIG. 7E represents data for disease sites treated with >70 Gy, FIG.7F represents disease sites treated with 60 Gy, and FIG. 7G representsdisease sites treated with 45 Gy.

Referring now to FIGS. 7A-7G, GARD, which is a clinical parameter thatintegrates a genomic patient-specific measure of tumor radiosensitivitywith physical RT dose, is introduced. GARD was calculated for 8,271patients using the RT dose and fractionation protocol that is standardfor each of the 20 disease sites in the cohort and ranked GARD valuesfrom highest to lowest. A higher GARD value predicts a higher RT effect.Three RT dose levels that are commonly utilized for sub-clinical (45Gy), microscopic (60 Gy) and macroscopic disease (>70 Gy) were used, asshown in FIG. 7A. Each RT dose level (45 Gy, 60 Gy≥70 Gy) wasrepresented by 30.4%, 59% and 10.6% of the patients in the TCC cohort.To facilitate the analysis, three GARD dose levels were defined tocorrespond to the same proportion of patients within each RT dose cohort(low 0-30.4^(th) percentile, middle 30.41th-89.4^(th) percentile, andhigh 89.41^(st)-100^(th) percentile). As shown in FIGS. 7A-7D, GARDreveals significant heterogeneity that results from uniform RI doseacross the TCC cohort. For example, although most of the patients thatare normally treated with 45 Gy are expected towards the bottom of theGARD scale (58% of patients in the lowest GARD level as shown in FIG.7D), a significant group is present near the middle of the scale (21% ofpatients in the middle GARD level as shown in FIG. 7C). The sameobservation is seen with patients treated with doses above 70 Gy. Themajority of these patients are at the very top of the distribution forGARD (34% of patients in the highest GARD level as shown in FIG. 7B),but a significant proportion of patients are found in the middle GARDlevel (11% of patients in the middle GARD level as shown in FIG. 7C).Finally, the largest patient subset (60 Gy) was distributed throughoutthe scale with patients in all three GARD dose levels as shown in FIGS.7B-7D. Thus, a higher dose does not always result in a higher doseeffect as predicted by GARD.

Next, each of the dose cohorts was evaluated individually for the TCCprotocol as shown in FIG. 7E-7G. Cervical cancer and oropharynx head andneck cancer had the highest GARD, consistent with the highradiocurability of these tumors. Importantly, GARD demonstrates that RTto 70 Gy has a higher predicted effect in oropharynx when compared tonon-oropharynx head and neck cancer (Median GARD 46.32 vs. 32.56,p=0.04), also consistent with known clinical data. In the group ofdisease sites normally treated to 60 Gy, GARD identifies glioma (medianGARD=16.55) and sarcoma (median GARD=17.94) as the two disease siteswith the least effect from uniform RT when compared to all other diseasesites at this dose level (p<0.0001). Furthermore, GARD also estimatesthat RT effect at 60 Gy is larger in non-melanoma skin cancer whencompared with melanoma (median GARD, non-melanoma vs. non-melanoma 25,80vs. 21.17, p=0.01). Finally at the 45 Gy dose level, GARD identifies ahigher RT dose effect for esophageal cancer, when compared with rectalcancer (p=0.0003). This is consistent with data for pre-operativechemoradiation where the pathological complete response to 5-FU-basedchemoradiation is higher in esophageal when compared with rectal cancer.In addition, GARD identifies a higher predicted RT effect for stomachcancer when compared with pancreas (p=0.002). Both of these diseasesites are commonly treated with post-operative RT, with evidence for ahigher RT impact in stomach.

To further evaluate GARD, it was tested for the Erasmus Breast CancerCohort where detailed information on RI dose delivered, genomicinformation and mature clinical outcome was available (Erasmus dataset).FIGS. 8A-8G illustrate a framework for genomic RT dose with reference tothe Erasmus Breast Cancer cohort described herein. GARD predicts forDistant Metastasis-Free Survival (DMFS) in Breast Cancer. GARD andBED_(2.88) were generated for 263 lymph node negative patients treatedwith surgery and post-operative RI to the whole breast with or without atumor cavity boost. FIG. 8A illustrates transforming physical radiationdose to genomic adjusted radiation dose (GARD). RT doses received byeach patient in the cohort ranged from 40 Gy to 75 Gy. These weredivided in three RT dose levels: low (black, 40-59 Gy, about 10% ofpatients), intermediate (white, 60-69 Gy, about 65% of patients) andhigh (gray, 70-75 Gy, about 25% of patients) and are represented asdiscrete uniform blocks with the size of each block proportional to thenumber of patients in each group. GARD values for each individualpatient in the cohort are presented ranked from the highest to lowestvalue. Each line in the GARD prism represents an individual patient andis colored based on the physical dose received by the patient. In FIGS.8B-8D, three GARD levels to correspond to the same proportion ofpatients represented for each RT dose range are defined. Pie charts areshown demonstrating the proportion of patients from each physical doselevel in each GARD level. All physical doses are represented in each ofthe GARD levels, FIG. 8B represents the distribution of physical dosesin the highest GARD level, FIG. 8C represents the distribution ofphysical doses in the middle GARD level, and FIG. 8D represents thedistribution of physical doses in the lowest GARD level. FIG. 8E shows aweak but significant correlation between GARD and BED_(2.88). FIG. 8Fdemonstrates that patients that achieve the GARD threshold dose levelhave an improved DMFS that is statistically significant. FIG. 8G showsthat BED_(2.88) does not predict for DMFS.

As shown in FIG. 8A, this cohort was treated with a wide range of RTtotal dose (40-75 Gy). Similarly to observations for the TCC protocol,transformation to genomic dose (GARD) revealed significant heterogeneityachieved within this RT dose range as shown in FIG. 8A with all RT dosecohorts represented throughout the GARD spectrum as shown in FIGS.8B-8D. To serve as a control, BED_(2.88) was also generated, assuming auniform parameter for radiosensitivity α/β=2.88). As shown in FIG. 8E,there was a weak but significant correlation between GARD and BED_(2.88)(R=0.25, p<0.0001). Patients that achieved the GARD-threshold dose levelfor this cohort (GARD≥38.9) have an improved distant-metastases freesurvival (DMFS) (FIG. 8F, HR=2.31 (1.25, 4.25), p=0.006). In contrast,BED_(2.88) did not predict for DMFS in univariable analysis (FIG. 8G,p=0.12). On multivariable analyses, GARD is an independent predictor ofoutcome (e.g., Table of FIG. 9, HR=2.11 (1.13, 3.94), p=0.01).

Referring now to FIG. 9, a table illustrating the multivariable analysisof GARD in the Erasmus Breast Cancer cohort is shown. GARD is treated asa dichotomous variable with a pre-specified cut-point at the 75^(th)percentile, GARD is an independent variable that predicts clinicaloutcome in breast cancer.

Finally, to compare GARD to RSI, backward elimination in themultivariable model fitting with candidate variables (ER/PR status, Tstage, age, GARD, BED_(2.88) and RSI) was used. GARD (p=0.008) was theonly remaining significant variable in the model.

Referring now to FIGS. 10A-10C, graphs illustrating genomically-informedRT. FIG. 10A illustrates that GARD provides a paradigm to inform RT doseclinical decisions based on individual tumor genomics. Physical dose isindividualized in patients that are genomically-identifiable based onRSI and adjusted until a pre-determined GARD threshold value isachieved. The physical dose required to meet the GARD threshold doselevel (GARD>38.9) is shown in FIG. 10A. As an example, a patient with anRSI value of 0.21 would require a dose of 50 Gy to meet the threshold asshown in FIG. 10A. In contrast, an RSI value of 0.27 would require adose of 60 Gy to achieve the same threshold as shown in FIG. 10A. Itshould be noted that this curve is based on the RT benefit calculatedfor DM (not local control). FIG. 10B illustrates the probability ofachieving the GARD threshold dose level (GARD>38.9) in an unselectedpopulation is shown as a function of physical dose. The proportion ofGARD-high patients increases from 5% to 36% in the dose range from 50 to76 Gy. FIG. 10C illustrates the potential therapeutic benefit of RT doseescalation is estimated using the estimates of GARD-high/lowsub-populations achieved at each physical dose of FIG. 10B andnormalized to the effect at 50 Gy. The GARD-based model predicts that amodest improvement in DMFS is maximized in a genomically-identifiablepopulation.

A GARD-based platform to inform RT dose can provide the ability toindividualize RT dose based on tumor genomics. RT dose can beindividualized for genomically-identifiable patients, to achieve apredetermined GARD threshold value associated with best clinicaloutcome. FIGS. 10A-10C illustrates this concept where dose is adjustedto account for tumor radiosensitivity. A patient subset (RSI=0.18-0.35)is identified that achieves the GARD threshold receiving doses from45-75 Gy as shown in FIGS. 10A and 10B. This subset represents 25% ofbreast cancer patients.

Next, the distribution of GARD-high/low sub-populations at each doselevel (e.g., as shown in FIG. 10B) was used to estimate the potentialbenefit of genomically-informed RT dose. As shown in FIG. 10C, it isestimated that RT dose escalation results in an overall slightimprovement in DMFS. However, these improvements would not be noticed inan unselected randomized trial. For example, the model estimates thatdose escalation from 50 to 66 Gy would result in a small decrease inDMFS (HR=0.92). A trial with 80% power to detect this difference withoutgenomic guidance would require 14,489 patients. In contrast, aGARD-directed trial targeting patients with the most potential forbenefit would require 230 patients.

EORTC 22881-10882 randomized 5,318 patients to post-operative wholebreast RT (50 Gy) with or without a 16 Gy boost^(26,27). Dose escalationresulted in a decrease in local recurrence risk (HR=0.59, 10-yrfollow-up, HR=0,65, 17.2-years median follow-up) and no difference in DMat 20 years (HR 1.06, 0.92-1.24, p=0.29). The estimated DMFS benefit fordose escalation calculated by GARD (HR=0.92) is in the same range asthese prospective results. Further, the estimated HR for DMFS isone-fourth the observed benefit for local recurrence (HR=0.65),consistent with the 4:1 relationship between local recurrence and breastcancer death observed in the EBCTCG meta-analysis²⁸. These datademonstrate that GARD can be utilized to design genomically-guidedclinical trials in radiation oncology.

Discussion

A feasible approach to precision medicine in radiation oncology isdescribed herein. GARD is a clinical parameter for genomic radiationdosing which allows the individualization of RT dose to match tumorradiosensitivity and provides a framework to design genomically-guidedclinical trials in radiation oncology.

The clinical validity of GARD is supported by several lines of evidence.First, GARD is based on RSI and the linear quadratic model, both ofwhich have extensive clinical validation. RSI has been validated as apredictor of outcome in multiple datasets of RT-treated patients, andthe LQ model has served as the basis for dose and fractionation inclinical radiation oncology. Second, it was demonstrated thatsignificant biological heterogeneity results from uniform one-size-fitsall RT dose consistent with the clinical heterogeneity of RT benefitseen in the clinic. For example, glioma and sarcoma had the lowest GARDmedian value for all disease sites. In addition, GARD predicts higher RTimpact in oropharynx HNC when compared with non-oropharynx, esophagealcancer when compared with rectal cancer, non-melanoma skin cancer whencompared with melanoma and gastric cancer when compared with pancreaticcancer. All of these observations are consistent with results fromclinical studies.

Third, the clinically utility of GARD was tested in a cohort of 263breast cancer patients treated with surgery and RT. This cohort is idealto test a radiation-related predictor since none of the patientsreceived chemotherapy and/or hormonal therapy, thus limiting confoundingfactors. In addition, there was significant heterogeneity in theradiation doses delivered to the tumor cavity. The analyses show thatGARD is an independent predictor of RT-specific outcome, outperformsboth RSI and BED_(2.88), and is, critically, clinically actionablethrough changes in RT dose. Furthermore, GARD was an independentpredictor of clinical outcome in four additional independent cohortsincluding breast, GBM, lung and pancreas cancer patients.

The techniques described herein have several important implications.First, integration of classical radiobiology and genomics demonstratesthat it is possible to identify genomically-distinct populations thatderive differential benefit from RT. Further, a method by which tocustomize radiation dose to match the radiosensitivity of an individualpatient has been provided. A framework to design genomically-stratified,RT-based trials using specifically defined genomic subpopulations hasbeen provided. This brings radiation oncology in line with modern trialdesign for targeted agents, and, like the discovery of imatinib allowedfor the age of targeted therapy²⁹, this heralds a new era ofgenomically-dosed RT. As shown herein, genomic-based clinical trialdesign can dramatically improve the efficiency of the clinical trials inradiation oncology. It can lead to a reduction in both the number ofpatients required to test a hypothesis and the time to complete thetrial, both of which should lead to significant cost-savings. Finally,this model is RT-focused rather than disease-site focused. It has beendemonstrated that wide heterogeneity in radiosensitivity across tumortypes, and both RSI and GARD have been shown to predict for clinicaloutcome in multiple disease-sites. Thus, this could provide a rationale,and indeed a roadmap, to genomically guided RT-dose optimization in allcancers.

There is clinical opportunity for patient-specific dose optimization inbreast cancer. RT doses have been empirically optimized leading toexcellent local control rates and toxicity for breast cancer, althoughthere are molecular sub-populations with higher risks for localrecurrence following standard doses (i.e.TN-radioresistant)^(17,30-32,33,34). The framework described hereinaccepts all prior dose optimization and provides a way to move forward.Genomic subpopulations that derive differential benefit from RT (RSI)can be identified. There are modest clinical differences between patientsubsets that are at least partly driven by RT dose effect (GARD). Sincethese differences only appear in specific subpopulations, they are notreadily apparent in unselected clinical trials. Third, GARD-based RTdosing provides an approach to determine the required physical doserange to achieve the GARD threshold. Importantly, the dose rangesproposed for a significant proportion of patients (25%) can be deliveredwhile respecting normal tissue constraints. Finally, this approachfocuses on distant metastasis (DM) not local control (LC) as theclinical endpoint. Solid clinical evidence from the Oxford meta-analysisnow demonstrate unequivocally that RT decreases the risk of deathpresumably by decreasing the risk of DM²⁸. Thus, there are stillunrealized clinical gains in breast cancer that may result fromunderstanding the impact of RT on the development of DM.

Several assumptions were made to complete the analyses described herein.Specifically, it was assumed that the recurrence risks and RSIdistribution in the Erasmus cohort is similar to a normal lymph nodenegative breast cancer population. This is strengthened by theobservation that the RSI distribution between Erasmus and TCC aresimilar. It was also assumed that the quadratic component of radiationresponse, β, is constant. As there has been no attempt to modeldifferent ranges of fractional (daily) dose, this assumption should notqualitatively affect the conclusions. Finally, while RSI was used in theanalyses, the calculation of GARD can use any measure ofradiosensitivity and/or be expanded to include other biologicalparameters involved in radiation response including hypoxia, DNA repair,proliferation and the immune system.

In conclusion, a central requirement for precision medicine in radiationoncology is the ability to inform radiation dose parameters to matchindividual tumor biology, thus delivering the right radiation dose forthe right patient. The genomic adjusted radiation dose (GARD) describedherein provides the ability to genomically-inform radiation dose and isalso a safe and feasible approach to precision radiation oncology.

REFERENCES

-   1. Barnett G C, West C M, Dunning A M, et al. Normal tissue    reactions to radiotherapy: towards tailoring treatment dose by    genotype. Nat Rev Cancer 2009; 9:134-42.-   2, Brown J M, Adler J R, Jr, Is Equipment Development Stifling    Innovation in Radiation Oncology? Int J Radiat Oncol Biol Phys 2015;    92:713-4.-   3, Roper N, Stensland K D, Hendricks R, Galsky M D. The landscape of    precision cancer medicine clinical trials in the United States.    Cancer Treat Rev 2015; 41:385-90.-   4. Paik S, Shak S, Tang G, et al. A multigene assay to predict    recurrence of tamoxifen-treated, node-negative breast cancer. N Engl    J Med 2004; 351:2817-26.-   5. van de Vijver M J, He Y D, van't Veer U, et al. A gene-expression    signature as a predictor of survival in breast cancer. N Engl J Med    2002; 347:1999-2009.-   6. Torres-Roca J F. A molecular assay of tumor radiosensitivity: a    roadmap towards biology-based personalized radiation therapy. Per    Med 2012; 9:547-57.-   7. Mendelsohn J, Tursz T, Schilsky R L, Lazar V. WIN    Consortium-challenges and advances. Nat Rev Clin Oncol 2011;    8:133-4.-   8. Tursz T, Andre F, Lazar V, Lacroix L, Soria J C. Implications of    personalized medicine-perspective from a cancer center. Nat Rev Clin    Oncol 2011; 8:177-83.-   9. Dalton W S, Friend S H. Cancer biomarkers—an invitation to the    table. Science 2006; 312:1165-8.-   10. Ahmed K A, Fulp W J, Berglund A E, et al. Differences Between    Colon Cancer Primaries and Metastases Using a Molecular Assay for    Tumor Radiation Sensitivity Suggest Implications for Potential    Oligometastatic SBRT Patient Selection. Int J Radiat Oncol Biol Phys    2015.-   11. Eschrich S, Fulp W J, Pawitan Y, et al. Validation of a    Radiosensitivity Molecular Signature in Breast Cancer. Clin Cancer    Res 2012.-   12. Eschrich S, Zhang H, Zhao H, et al, Systems biology modeling of    the radiation sensitivity network: a biomarker discovery platform.    Int J Radiat Oncol Biol Phys 2009; 75:497-505.-   13. Eschrich S A, Pramana J, Zhang H, et al. A gene expression model    of intrinsic tumor radiosensitivity: prediction of response and    prognosis after chemoradiation. Int J Radiat Oncol Biol Phys 2009;    75:489-96.-   14. Strom T, Hoffe S E, Fulp W, et al. Radiosensitivity index    predicts for survival with adjuvant radiation in resectable    pancreatic cancer. Radiother Oncol 2015.-   15. Torres-Roca J F, Eschrich S, Zhao H, et al. Prediction of    Radiation Sensitivity Using a Gene Expression Classifier. Cancer Res    2005; 65:7169-76.-   16. Torres Roca J F, Erho N, Vergara I, et al. A Molecular Signature    of Radiosensitivity (RSI) is an RT-specific Biomarker in Prostate    Cancer. ASTRO; 2014; San Francisco: International Journal of    Radiation Oncology Biology Physics. p. 5157.-   17. Torres-Roca J F, Fulp W, Naghavi A O, et al. Integrating a    Molecular Signature of Intrinsic Radiosensitivity into the    Classification of Breast Cancer. Int I Radiat Oncol Biol Phys 2015:    in press.-   18. Ahmed K A, Eschrich S, Torres Roca J F, Caudell J J. The    Radiosensitivity Index Predicts For Overall Survival in    Glioblastoma. Oncotarget 2015: in press.-   19. Creelan B, Eschrich S A, Fulp W J, Torres Roca J F. A Gene    Expression Platform to Predict Benefit From Adjuvant External Beam    Radiation in Resected Non-Small Lung Cancer. ASTRO; 2014; San    Francisco: International Journal of Radiation Oncology Biology    Physics. p. 576.-   20. Fenstermacher D A, Wenham R M, Rollison D E, Dalton W S.    Implementing personalized medicine in a cancer center. Cancer J    2011; 17:528-36.-   21. Welsh E A, Eschrich S A, Berglund A E, Fenstermacher D A.    Iterative rank-order normalization of gene expression microarray    data. BMC Bioinformatics 2013; 14:153.-   22. Eschrich S A, Fulp W J, Pawitan Y, et al. Validation of a    radiosensitivity molecular signature in breast cancer. Clin Cancer    Res 20:12; 18:5134-43.-   23. Fowler J F. 21 years of biologically effective dose. The British    journal of radiology 2010; 83:554-68.-   24. Qi X S, White J, Li X A. Is alpha/beta for breast cancer really    low? Radiother Oncol 2011; 100:282-8.-   25. Jeong J, Shoghi K I, Deasy J O, Modelling the interplay between    hypoxia and proliferation in radiotherapy tumour response. Phys Med    Biol 2013; 58:4897-919.-   26. Bartelink H, Horiot J C, Poortmans P M, et al. Impact of a    higher radiation dose on local control and survival in    breast-conserving therapy of early breast cancer: 10-year results of    the randomized boost versus no boost EORTC 22881-10882 trial. J Clin    Oncol 2007; 25:3259-65.-   27. Bartelink H, Maingon P, Poortmans P, et al. Whole-breast    irradiation with or without a boost for patients treated with    breast-conserving surgery for early breast cancer: 20-year follow-up    of a randomised phase 3 trial. Lancet Oncol 2015; 16:47-56.-   28. Darby S, McGale P, Correa C, et al. Effect of radiotherapy after    breast-conserving surgery on 10-year recurrence and 15-year breast    cancer death: meta-analysis of individual patient data for 10,801    women in 17 randomised trials. Lancet 2011; 378:1707-16.-   29. Druker B J, Guilhot F, O'Brien S G, et al. Five-year follow-up    of patients receiving imatinib for chronic myeloid leukemia. N Engl    J Med 2006; 355:2408-17.-   30. Abdulkarim B S, Cuartero J, Hanson J, Deschenes J, Lesniak D,    Sabri S. Increased risk of locoregional recurrence for women with    T1-2N0 triple-negative breast cancer treated with modified radical    mastectomy without adjuvant radiation therapy compared with    breast-conserving therapy. J Clin Oncol 2011; 29:2852-8.-   31. Voduc K D, Cheang M C, Tyldesley S, Gelmon K, Nielsen T O,    Kennecke H. Breast cancer subtypes and the risk of local and    regional relapse, J Clin Oncol 2010; 28:1684-91.-   32. Arvold N D, Taghian A G, Niemierko A, et al. Age, breast cancer    subtype approximation, and local recurrence after breast-conserving    therapy. J Clin Oncol 2011; 29:3885-91.-   33. Lowery A J, Kell M R, Glynn R W, Kerin M J, Sweeney K J.    Locoregional recurrence after breast cancer surgery: a systematic    review by receptor phenotype. Breast Cancer Res Treat 2012;    133:831-41.-   34. Adkins F C, Gonzalez-Angulo A M, Lei X, et al. Triple-negative    breast cancer is not a contraindication for breast conservation. Ann    Surg Oncol 2011; 18:3164-73.

Although the subject matter has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the subject matter defined in the appended claims is notnecessarily limited to the specific features or acts described above.Rather, the specific features and acts described above are disclosed asexample forms of implementing the claims.

1. A method of treating a subject having a tumor, comprising:determining a radiosensitivity index of the tumor, the radiosensitivityindex being assigned from expression levels of one or more signaturegenes; deriving a subject-specific variable based on theradiosensitivity index; obtaining a genomic adjusted radiation doseeffect value for the tumor, the genomic adjusted radiation dose effectvalue being predictive of tumor recurrence in the subject aftertreatment; and determining a radiation dose based on thesubject-specific variable and the genomic adjusted radiation dose effectvalue.
 2. The method of claim 1, further comprising administeringradiation therapy to the subject.
 3. The method of claim 1, whereindetermining a radiation dose further comprises determining a radiationdose per treatment or a number of radiation treatments.
 4. The method ofclaim 1, wherein the genomic adjusted radiation dose effect value forthe tumor comprises a range of values predictive of tumor recurrence inthe subject after treatment.
 5. The method of claim 1, wherein thegenomic adjusted radiation dose effect value for the tumor is indicativeof a low chance of tumor recurrence in the subject after treatment. 6.The method of claim 1, wherein the genomic adjusted radiation doseeffect value for the tumor is specific to a type of cancer.
 7. Themethod of claim 6, wherein the type of cancer comprises breast, lung,prostate, glioblastoma, head and neck, pancreas, esophagus, orcolorectal cancer.
 8. The method of claim 1, further comprisingdetermining the genomic adjusted radiation dose effect value using aunivariate or multivariate analysis of genomic dose effect value andoutcome for a group of subjects.
 9. The method of claim 1, wherein thesubject-specific variable provides a measure of the tumor's ability toaccumulate radiation damage.
 10. The method claim 1, wherein thesubject-specific variable is derived using a linear quadratic model forcell survival, the radiosensitivity index being approximately equal tocell survival.
 11. The method of claim 1, wherein the one or moresignature genes comprise at least one of Androgen receptor (AR); Junoncogene (c-Jun); Signal transducer and activator of transcription 1(STAT1); Protein kinase C, beta (PRKCB or PKC); V-relreticuloendotheliosis viral oncogene homolog A (avian) (RELA or p65);c-Abl oncogene 1, receptor tyrosine kinase (ABL1 or c-Abl); SMT3suppressor of mif two 3 homolog 1 (S cerevisiae) (SUMO1); p21(CDKN1A)-activated kinase 2 (PAK2); Histone deacetylase 1 (HDAC1); orInterferon regulatory factor 1 (IRF1).
 12. A system for developing aradiation therapy treatment plan for a subject having a tumor,comprising: a processor; and a memory operably coupled to the processor,the memory having computer-executable instructions stored thereon that,when executed by the processor, cause the processor to: determine aradiosensitivity index of the tumor, the radiosensitivity index beingassigned from expression levels of one or more signature genes; derive asubject-specific variable based on the radiosensitivity index; obtain agenomic adjusted radiation dose effect value for the tumor, the genomicadjusted radiation dose effect value being predictive of tumorrecurrence in the subject after treatment; and determine a radiationdose based on the subject-specific variable and the genomic adjustedradiation dose effect value.
 13. The system of claim 12, whereindetermining a radiation dose comprises determining a number of radiationtreatments.
 14. The system of claim 12, wherein the genomic adjustedradiation dose effect value for the tumor comprises a range of valuespredictive of tumor recurrence in the subject after treatment.
 15. Thesystem of claim 12, wherein the genomic adjusted radiation dose effectvalue for the tumor is indicative of a low chance of tumor recurrence inthe subject after treatment.
 16. The system of claim 12, wherein thegenomic adjusted radiation dose effect value for the tumor is specificto a type of cancer.
 17. The system of claim 16, wherein the type ofcancer comprises breast, lung, prostate, glioblastoma, head and neck,pancreas, esophagus, or colorectal cancer.
 18. The system of claim 12,wherein the memory has further computer-executable instructions storedthereon that, when executed by the processor, cause the processor todetermine the genomic adjusted radiation dose effect value using aunivariate or multivariate analysis of genomic dose effect value andoutcome for a group of subjects.
 19. The system of claim 12, wherein thesubject-specific variable provides a measure of the tumor's ability toaccumulate radiation damage.
 20. The system of claim 12, wherein thesubject-specific variable is derived using a linear quadratic model forcell survival, the radiosensitivity index being approximately equal tocell survival.
 21. The system of claim 12, wherein the one or moresignature genes comprise at least one of Androgen receptor (AR); Junoncogene (c-Jun); Signal transducer and activator of transcription 1(STAT1); Protein kinase C, beta (PRKCB or PKC); V-relreticuloendotheliosis viral oncogene homolog A (avian) (RELA or p65);c-Abl oncogene 1, receptor tyrosine kinase (ABL1 or c-Abl); SMT3suppressor of mif two 3 homolog 1 (S cerevisiae) (SUMO1); p21(CDKN1A)-activated kinase 2 (PAK2); Histone deacetylase 1 (HDAC1); orInterferon regulatory factor 1 (IRF1).